用时间序列观测数据对混合结果进行因果推理的贝叶斯多因素分析模型。

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Pantelis Samartsidis, Shaun R Seaman, Abbie Harrison, Angelos Alexopoulos, Gareth J Hughes, Christopher Rawlinson, Charlotte Anderson, André Charlett, Isabel Oliver, Daniela De Angelis
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引用次数: 0

摘要

在许多科学研究领域,利用多单位和结果的时间序列观测数据来评估干预措施的影响是一个常见的问题。在这里,我们提出了一种新的贝叶斯多元因素分析模型来估计这种情况下的干预效果,并开发了一种有效的马尔可夫链蒙特卡罗算法来从高维和不可处理的后验中采样。所提出的方法是为数不多的能够同时处理混合类型(连续、二项、计数)结果的方法之一,通过联合建模受干预影响的多个结果来提高因果效应估计的效率,并易于为所有感兴趣的因果估计提供不确定性量化。使用建议的方法,我们评估了地方追踪伙伴关系对英格兰COVID-19测试和追踪计划有效性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.

Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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